Construct validation data according to training data

lgb.Dataset.create.valid(
  dataset,
  data,
  info = list(),
  label = NULL,
  weight = NULL,
  group = NULL,
  init_score = NULL,
  params = list(),
  ...
)

Arguments

dataset

lgb.Dataset object, training data

data

a matrix object, a dgCMatrix object, a character representing a path to a text file (CSV, TSV, or LibSVM), or a character representing a path to a binary Dataset file

info

a list of information of the lgb.Dataset object. NOTE: use of info is deprecated as of v3.3.0. Use keyword arguments (e.g. init_score = init_score) directly.

label

vector of labels to use as the target variable

weight

numeric vector of sample weights

group

used for learning-to-rank tasks. An integer vector describing how to group rows together as ordered results from the same set of candidate results to be ranked. For example, if you have a 100-document dataset with group = c(10, 20, 40, 10, 10, 10), that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, etc.

init_score

initial score is the base prediction lightgbm will boost from

params

a list of parameters. See The "Dataset Parameters" section of the documentation for a list of parameters and valid values. If this is an empty list (the default), the validation Dataset will have the same parameters as the Dataset passed to argument dataset.

...

additional lgb.Dataset parameters. NOTE: As of v3.3.0, use of ... is deprecated. Add parameters to params directly.

Value

constructed dataset

Examples

# \donttest{ data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) # parameters can be changed between the training data and validation set, # for example to account for training data in a text file with a header row # and validation data in a text file without it train_file <- tempfile(pattern = "train_", fileext = ".csv") write.table( data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L)) , file = train_file , sep = "," , col.names = TRUE , row.names = FALSE , quote = FALSE ) valid_file <- tempfile(pattern = "valid_", fileext = ".csv") write.table( data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L)) , file = valid_file , sep = "," , col.names = FALSE , row.names = FALSE , quote = FALSE ) dtrain <- lgb.Dataset( data = train_file , params = list(has_header = TRUE) ) dtrain$construct()
#> [LightGBM] [Info] Construct bin mappers from text data time 0.00 seconds
dvalid <- lgb.Dataset( data = valid_file , params = list(has_header = FALSE) ) dvalid$construct()
#> [LightGBM] [Info] Construct bin mappers from text data time 0.00 seconds
# }